# HG changeset patch # User SylvainPL # Date 1270166954 14400 # Node ID 067e747fd9c05eac4847aa3f146fa16fbff1d0ba # Parent 60e82846a10d82ba899cbf3e6afe6e10adaf2d9d Ajout de noms differents pour les series produites pour differents choix de pretrain diff -r 60e82846a10d -r 067e747fd9c0 deep/stacked_dae/v_sylvain/nist_sda_retrieve.py --- a/deep/stacked_dae/v_sylvain/nist_sda_retrieve.py Thu Apr 01 17:47:53 2010 -0400 +++ b/deep/stacked_dae/v_sylvain/nist_sda_retrieve.py Thu Apr 01 20:09:14 2010 -0400 @@ -55,8 +55,20 @@ n_outs = 62 # 10 digits, 26*2 (lower, capitals) examples_per_epoch = NIST_ALL_TRAIN_SIZE + #To be sure variables will not be only in the if statement + PATH = '' + nom_reptrain = '' + nom_serie = "" + if state['pretrain_choice'] == 0: + PATH=PATH_NIST + nom_pretrain='NIST' + nom_serie="series_NIST.h5" + elif state['pretrain_choice'] == 1: + PATH=PATH_P07 + nom_pretrain='P07' + nom_serie="series_P07.h5" - series = create_series(state.num_hidden_layers) + series = create_series(state.num_hidden_layers,nom_serie) print "Creating optimizer with state, ", state @@ -83,12 +95,6 @@ ## "or reduce the number of pretraining epoch to run the code (better idea).\n") ## print('\n\tpretraining with P07') ## optimizer.pretrain(datasets.nist_P07(min_file=0,max_file=nb_file)) - if state['pretrain_choice'] == 0: - PATH=PATH_NIST - nom_pretrain='NIST' - elif state['pretrain_choice'] == 1: - PATH=PATH_P07 - nom_pretrain='P07' print ('Retrieve pre-train done earlier ( '+nom_pretrain+' )') @@ -168,7 +174,7 @@ # These Series objects are used to save various statistics # during the training. -def create_series(num_hidden_layers): +def create_series(num_hidden_layers, nom_serie): # Replace series we don't want to save with DummySeries, e.g. # series['training_error'] = DummySeries() @@ -177,7 +183,7 @@ basedir = os.getcwd() - h5f = tables.openFile(os.path.join(basedir, "series.h5"), "w") + h5f = tables.openFile(os.path.join(basedir, nom_serie), "w") # reconstruction reconstruction_base = \